An Investigation into the impact of the Feature Subset Selection Methods for Classification of Gene Expression Profiles of Microarray Dataset

In the field of machine learning and pattern recognition, feature subset selection is an important area, where many approaches have been proposed. In this paper, the authors have chosen six feature selection algorithms and analyzed their performance using only one dataset called colon tumor dataset from the public domain. The authors selected the reduced number of features 10, 20, 30, 40 and 50 and calculated their accuracy with respect to the number of features, compared and analyzed with six feature selection algorithms. Hence, the author has advanced a contemporary gene expression data based on machine learning with the help of six feature selection algorithms. The same authors has already published their research work of selecting data with six algorithms. This paper extends their research work further on to performance metrics.